Using a micro-UAV for ultra-high resolution multi-sensor observations of Antarctic moss beds

This study is the first to use an Unmanned Aerial Vehicle (UAV) for mapping moss beds in Antarctica. Mosses can be used as indicators for the regional effects of climate change. Mapping and monitoring their extent and health is therefore important. UAV aerial photography provides ultra-high resolution spatial data for this purpose. We developed a technique to extract an extremely dense 3D point cloud from overlapping UAV aerial photography based on structure from motion (SfM) algorithms. The combination of SfM and patch-based multi-view stereo image vision algorithms resulted in a 2 cm resolution digital terrain model (DTM). This detailed topographic information combined with vegetation indices derived from a 6-band multispectral sensor enabled the assessment of moss bed health. This novel UAV system has allowed us to map different environmental characteristics of the moss beds at ultra-high resolution providing us with a better understanding of these fragile Antarctic ecosystems. The paper provides details on the different UAV instruments and the image processing framework resulting in DEMs, vegetation indices, and terrain derivatives.

[1]  Craig S. T. Daughtry,et al.  Acquisition of NIR-Green-Blue Digital Photographs from Unmanned Aircraft for Crop Monitoring , 2010, Remote. Sens..

[2]  John Turner,et al.  Antarctic climate change and the environment , 2009, Antarctic Science.

[3]  R. Dunford,et al.  Potential and constraints of Unmanned Aerial Vehicle technology for the characterization of Mediterranean riparian forest , 2009 .

[4]  Guoqing Zhou,et al.  Foreword to the Special Issue on Unmanned Airborne Vehicle (UAV) Sensing Systems for Earth Observations , 2009, IEEE Trans. Geosci. Remote. Sens..

[5]  Ryosuke Shibasaki,et al.  UAV-Borne 3-D Mapping System by Multisensor Integration , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Pablo J. Zarco-Tejada,et al.  Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Albert Rango,et al.  Texture and Scale in Object-Based Analysis of Subdecimeter Resolution Unmanned Aerial Vehicle (UAV) Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Richard Szeliski,et al.  Modeling the World from Internet Photo Collections , 2008, International Journal of Computer Vision.

[9]  Frédéric Baret,et al.  Assessment of Unmanned Aerial Vehicles Imagery for Quantitative Monitoring of Wheat Crop in Small Plots , 2008, Sensors.

[10]  J. Ponce,et al.  Accurate, Dense, and Robust Multi-View Stereopsis , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Sharon A. Robinson,et al.  Some like it wet - biological characteristics underpinning tolerance of extreme water stress events in Antarctic bryophytes. , 2006, Functional plant biology : FPB.

[12]  P. Hardin,et al.  An Unmanned Aerial Vehicle for Rangeland Photography , 2005 .

[13]  J. Turner,et al.  Antarctic climate change during the last 50 years , 2005 .

[14]  David G. Lowe,et al.  Distinctive Image Features from Scale-Invariant Keypoints , 2004, International Journal of Computer Vision.

[15]  Sharon A. Robinson,et al.  Living on the edge – plants and global change in continental and maritime Antarctica , 2003 .

[16]  Sharon A. Robinson,et al.  Surface reflectance properties of Antarctic moss and their relationship to plant species, pigment composition and photosynthetic function , 2002 .

[17]  Frank Dellaert,et al.  Structure from motion without correspondence , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[18]  Jose A. Jiménez-Berni,et al.  A new era in remote sensing of crops with unmanned robots , 2008 .

[19]  Noah Snavely.  Photo Tourism : Exploring image collections in 3D , 2006 .